Children with autism struggle to recognize facial expressions, make eye contact, and engage in social interactions. Many of these children can have dramatic recoveries, particularly if social skills are taught from an early age. However, in today's healthcare system the delivery of the behavioral intervention is bottlenecked by a sharp and increasing imbalance in the number of behavioral therapists and the number of children in need of care. As such, there is an urgent need to develop mobilized methods of care delivery. We have developed an artificial intelligence tool for automatic facial expression recognition that runs on Google Glass through an Android app and delivers instantaneous social cues to individuals with autism in their natural environment, providing therapy that today is given only by clinicians in non-scalable person-to- person sessions. The system leverages Glass's outward facing camera to read a person's facial expressions and passes facial landmarks to an Android native app for immediate machine learning-based emotion classification. The system then gives the child wearer real-time social cues and records social responses. We believe that the system's ability to provide continuous behavioral therapy outside of clinical settings will enable dramatically faster gains in social acuity that will, within a limited and self-directed period of use, permit the child to engage in social settings on his/her own. This proposal outlines three main aims needed to test, refine and optimize the tools and a series of validation experiments needed to bring our system from prototype to a viable clinical tool that every family can use regularly from home for precision healthcare.